My journey to co-founding entourage

Back in 2017, AI was still quite a niche area. It was more focused on deep learning, particularly Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs). If you are part of that cohort, you’ll fondly remember OpenAI’s website having all of these agents chasing each other in mazes playing various games as they explored reinforcement learning.

At the time I was running miDrive, a startup that I had raised ~$10m for, focused on the EdTech sector - bringing learning to drive into the digital age (and all of the transactions that go along with it: first car, insurance, etc.). After much reflection, I had grown the company to a stage that I could feel proud about, and completed much of the innovative product work. And I couldn’t help but keep being drawn to artificial intelligence at the weekend.

Always being a founder, and thinking a few horizons out - I knew that AI would be the transformational technology of my lifetime. And I wanted to be living and breathing it.

That’s when I made a calculated bet to go to a large corporate (with deep pockets) and no external pressures other than focusing on learning and building my expertise amongst some of the world’s leading scientists. I joined a large asset manager as global head of AI products, and was tasked with infusing AI, ML, and NLP throughout the portfolio management.

Armed with deep technical knowledge and battle-tested experience deploying AI at scale, I was ready to return to my founder roots. The years as a senior executive at a large publicly traded company at the forefront of AI adoption had given me something invaluable: credibility that would matter when fundraising, coupled with insights into the real challenges enterprises face when implementing AI systems.

That journey led to entourage. I’ve always admired systems that get smarter through collaboration. Think of open-source projects where one coder’s fix helps thousands. Now apply that to AI agents. Most agents today work in silos. They tackle problems, but their lessons vanish after the task.

We started entourage to fix that. Our core idea is a shared memory protocol for AI agents. Agents capture experiences during tasks—successes, failures, patterns—and assimilate them into reusable knowledge. This isn’t offline training; it’s real-time learning embedded in the work. Retrieve what’s needed based on context, and the whole network improves.